Forecast-in-a-Box: AI weather forecasting, easy to run and simple to deploy

Traditional weather forecasting relies on large scale numerical simulations that run on high-performance computing systems. These methods require substantial computational resources, involve complex workflows, and generate large volumes of data that often exceed individual user needs. Forecast-in-a-Box leverages advances in data-driven modelling to greatly reduce computational and energy costs while delivering tailored forecast products directly to users. Partly funded from the European Commission’s Destination Earth initiative, it packages the entire forecasting chain into a simple and user-friendly application. Built on the open-source Anemoi¹ and Earthkit² projects, it offers a reproducible and modular environment that integrates data access, model execution, and visualisation. This enables accurate forecasts that can be run locally on user desktops, on premise computing infrastructure, or in the cloud.

The approach is being evaluated through a World Meteorological Organization (WMO) Integrated Processing and Prediction System (WIPPS) pilot project led by the Norwegian Meteorological Institute (MET Norway). In this project, a fully packaged forecasting system based on affordable hardware is provided to the Malawi Department of Climate Change and Meteorological Services (DCCMS). The forecasting system is driven by Forecast-in-a-Box and leverages MET Norway’s Bris³ model (Norwegian word for “light wind), a high-resolution data driven weather forecasting model built using the Anemoi framework. The solution is designed to be largely self-contained, with the only external dependency being the retrieval of ECMWF analysis dataset for forecast initialisation.

¹https://anemoi.readthedocs.io/en/latest/

²https://earthkit.ecmwf.int

³https://lumi-supercomputer.eu/data-driven-weather-forecasting-model/